Overview

Dataset statistics

Number of variables23
Number of observations490962
Missing cells980843
Missing cells (%)8.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.2 MiB
Average record size in memory184.0 B

Variable types

Numeric9
Categorical3
DateTime3
Text6
Unsupported2

Alerts

ACCESSORIAL_CHARGE is highly overall correlated with TOTAL_PAIDHigh correlation
CARRIER_NAME2 is highly overall correlated with DUTIES_AND_TAXES_CHARGES and 2 other fieldsHigh correlation
DUTIES_AND_TAXES_CHARGES is highly overall correlated with CARRIER_NAME2High correlation
HEIGHT is highly overall correlated with SHIP_WEIGHT and 1 other fieldsHigh correlation
LENGTH is highly overall correlated with SHIP_WEIGHT and 1 other fieldsHigh correlation
MODE is highly overall correlated with CARRIER_NAME2 and 1 other fieldsHigh correlation
SHIP_WEIGHT is highly overall correlated with HEIGHT and 2 other fieldsHigh correlation
TOTAL_PAID is highly overall correlated with ACCESSORIAL_CHARGE and 1 other fieldsHigh correlation
Unnamed: 0 is highly overall correlated with CARRIER_NAME2High correlation
WIDTH is highly overall correlated with HEIGHT and 2 other fieldsHigh correlation
CARRIER_NAME2 is highly imbalanced (71.1%)Imbalance
SHIP_WEIGHT_UNIT_OF_MEASURE is highly imbalanced (68.5%)Imbalance
DELIVERY_DATE has 24481 (5.0%) missing valuesMissing
DESTINATION_CITY has 9362 (1.9%) missing valuesMissing
DESTINATION_RECEIVER_NAME2 has 21624 (4.4%) missing valuesMissing
DESTINATION_ZIP has 9393 (1.9%) missing valuesMissing
HEIGHT has 294158 (59.9%) missing valuesMissing
LENGTH has 294158 (59.9%) missing valuesMissing
SHIP_DATE has 14209 (2.9%) missing valuesMissing
SHIP_DATE_DAY_YEAR has 14209 (2.9%) missing valuesMissing
WIDTH has 294158 (59.9%) missing valuesMissing
ACCESSORIAL_CHARGE is highly skewed (γ1 = -32.24228866)Skewed
DUTIES_AND_TAXES_CHARGES is highly skewed (γ1 = 33.58318443)Skewed
TOTAL_PAID is highly skewed (γ1 = 28.92348878)Skewed
DESTINATION_ZIP is an unsupported type, check if it needs cleaning or further analysisUnsupported
ORIGIN_ZIP is an unsupported type, check if it needs cleaning or further analysisUnsupported
ACCESSORIAL_CHARGE has 291490 (59.4%) zerosZeros
DUTIES_AND_TAXES_CHARGES has 459405 (93.6%) zerosZeros
SHIP_WEIGHT has 13078 (2.7%) zerosZeros
TOTAL_PAID has 37073 (7.6%) zerosZeros

Reproduction

Analysis started2024-02-16 08:23:05.819526
Analysis finished2024-02-16 08:24:28.347539
Duration1 minute and 22.53 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION 

Distinct128660
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58477.814
Minimum0
Maximum128659
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2024-02-16T00:24:28.522644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4909
Q127150.25
median57835.5
Q388520.75
95-th percentile115896.95
Maximum128659
Range128659
Interquartile range (IQR)61370.5

Descriptive statistics

Standard deviation35692.281
Coefficient of variation (CV)0.61035595
Kurtosis-1.1517576
Mean58477.814
Median Absolute Deviation (MAD)30685.5
Skewness0.08844712
Sum2.8710384 × 1010
Variance1.273939 × 109
MonotonicityNot monotonic
2024-02-16T00:24:28.939063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
< 0.1%
9429 5
 
< 0.1%
9418 5
 
< 0.1%
9419 5
 
< 0.1%
9420 5
 
< 0.1%
9421 5
 
< 0.1%
9422 5
 
< 0.1%
9423 5
 
< 0.1%
9424 5
 
< 0.1%
9425 5
 
< 0.1%
Other values (128650) 490912
> 99.9%
ValueCountFrequency (%)
0 5
< 0.1%
1 5
< 0.1%
2 5
< 0.1%
3 5
< 0.1%
4 5
< 0.1%
5 5
< 0.1%
6 5
< 0.1%
7 5
< 0.1%
8 5
< 0.1%
9 5
< 0.1%
ValueCountFrequency (%)
128659 1
< 0.1%
128658 1
< 0.1%
128657 1
< 0.1%
128656 1
< 0.1%
128655 1
< 0.1%
128654 1
< 0.1%
128653 1
< 0.1%
128652 1
< 0.1%
128651 1
< 0.1%
128650 1
< 0.1%

ACCESSORIAL_CHARGE
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct9746
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.881376
Minimum-13210.207
Maximum3074.76
Zeros291490
Zeros (%)59.4%
Negative6590
Negative (%)1.3%
Memory size3.7 MiB
2024-02-16T00:24:29.303117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-13210.207
5-th percentile0
Q10
median0
Q34.32306
95-th percentile51.1146
Maximum3074.76
Range16284.967
Interquartile range (IQR)4.32306

Descriptive statistics

Standard deviation65.637867
Coefficient of variation (CV)5.5244333
Kurtosis8275.9079
Mean11.881376
Median Absolute Deviation (MAD)0
Skewness-32.242289
Sum5833304
Variance4308.3296
MonotonicityNot monotonic
2024-02-16T00:24:29.541222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 291490
59.4%
1.1826 26029
 
5.3%
1.69506 6640
 
1.4%
1.27458 6409
 
1.3%
22.338 5600
 
1.1%
2.628 5060
 
1.0%
0.0657 4091
 
0.8%
11.1033 3940
 
0.8%
4.32306 3730
 
0.8%
39.42 3638
 
0.7%
Other values (9736) 134335
27.4%
ValueCountFrequency (%)
-13210.20702 1
< 0.1%
-12053.79504 2
< 0.1%
-2169.97902 1
< 0.1%
-1719.67122 1
< 0.1%
-1717.25346 1
< 0.1%
-1490.06286 2
< 0.1%
-1473.1254 1
< 0.1%
-1405.46754 2
< 0.1%
-1240.65252 2
< 0.1%
-1145.55834 1
< 0.1%
ValueCountFrequency (%)
3074.76 1
< 0.1%
2979.495 1
< 0.1%
2794.1553 1
< 0.1%
2693.7 2
< 0.1%
2681.874 1
< 0.1%
2601.72 1
< 0.1%
2588.58 1
< 0.1%
2584.1124 2
< 0.1%
2557.7667 1
< 0.1%
2424.33 2
< 0.1%

CARRIER_NAME2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
Nicole Brooks
436936 
Timothy Parks
44852 
Randall Hall
 
8568
Kimberly Flynn
 
606

Length

Max length14
Median length13
Mean length12.983783
Min length12

Characters and Unicode

Total characters6374544
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRandall Hall
2nd rowRandall Hall
3rd rowRandall Hall
4th rowRandall Hall
5th rowRandall Hall

Common Values

ValueCountFrequency (%)
Nicole Brooks 436936
89.0%
Timothy Parks 44852
 
9.1%
Randall Hall 8568
 
1.7%
Kimberly Flynn 606
 
0.1%

Length

2024-02-16T00:24:29.752844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T00:24:29.925301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nicole 436936
44.5%
brooks 436936
44.5%
timothy 44852
 
4.6%
parks 44852
 
4.6%
randall 8568
 
0.9%
hall 8568
 
0.9%
kimberly 606
 
0.1%
flynn 606
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 1355660
21.3%
490962
 
7.7%
r 482394
 
7.6%
i 482394
 
7.6%
k 481788
 
7.6%
s 481788
 
7.6%
l 472420
 
7.4%
e 437542
 
6.9%
N 436936
 
6.9%
c 436936
 
6.9%
Other values (15) 815724
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4901658
76.9%
Uppercase Letter 981924
 
15.4%
Space Separator 490962
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1355660
27.7%
r 482394
 
9.8%
i 482394
 
9.8%
k 481788
 
9.8%
s 481788
 
9.8%
l 472420
 
9.6%
e 437542
 
8.9%
c 436936
 
8.9%
a 70556
 
1.4%
y 46064
 
0.9%
Other values (6) 154116
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
N 436936
44.5%
B 436936
44.5%
P 44852
 
4.6%
T 44852
 
4.6%
R 8568
 
0.9%
H 8568
 
0.9%
K 606
 
0.1%
F 606
 
0.1%
Space Separator
ValueCountFrequency (%)
490962
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5883582
92.3%
Common 490962
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1355660
23.0%
r 482394
 
8.2%
i 482394
 
8.2%
k 481788
 
8.2%
s 481788
 
8.2%
l 472420
 
8.0%
e 437542
 
7.4%
N 436936
 
7.4%
c 436936
 
7.4%
B 436936
 
7.4%
Other values (14) 378788
 
6.4%
Common
ValueCountFrequency (%)
490962
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6374544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1355660
21.3%
490962
 
7.7%
r 482394
 
7.6%
i 482394
 
7.6%
k 481788
 
7.6%
s 481788
 
7.6%
l 472420
 
7.4%
e 437542
 
6.9%
N 436936
 
6.9%
c 436936
 
6.9%
Other values (15) 815724
12.8%

DELIVERY_DATE
Date

MISSING 

Distinct1458
Distinct (%)0.3%
Missing24481
Missing (%)5.0%
Memory size3.7 MiB
Minimum2019-06-18 00:00:00
Maximum2024-01-25 00:00:00
2024-02-16T00:24:30.124907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:30.505102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DESTINATION_CITY
Text

MISSING 

Distinct4291
Distinct (%)0.9%
Missing9362
Missing (%)1.9%
Memory size3.7 MiB
2024-02-16T00:24:30.868158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length29
Mean length8.2117359
Min length2

Characters and Unicode

Total characters3954772
Distinct characters76
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1118 ?
Unique (%)0.2%

Sample

1st rowDESOTO
2nd rowDESOTO
3rd rowSAINT ALBANS
4th rowBROKEN ARROW
5th rowBROKEN ARROW
ValueCountFrequency (%)
san 148175
22.2%
diego 147656
22.2%
fairburn 79894
12.0%
desoto 39044
 
5.9%
mabank 34298
 
5.1%
ontario 16402
 
2.5%
broken 7441
 
1.1%
arrow 7436
 
1.1%
houston 6885
 
1.0%
oceanside 6455
 
1.0%
Other values (3648) 172738
25.9%
2024-02-16T00:24:31.407979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 430200
10.9%
N 380637
 
9.6%
O 359818
 
9.1%
E 336220
 
8.5%
I 319754
 
8.1%
R 274467
 
6.9%
S 261561
 
6.6%
D 233915
 
5.9%
184862
 
4.7%
G 177345
 
4.5%
Other values (66) 995993
25.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3635632
91.9%
Space Separator 184862
 
4.7%
Lowercase Letter 132431
 
3.3%
Other Punctuation 912
 
< 0.1%
Dash Punctuation 642
 
< 0.1%
Decimal Number 247
 
< 0.1%
Open Punctuation 26
 
< 0.1%
Close Punctuation 16
 
< 0.1%
Connector Punctuation 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 430200
11.8%
N 380637
10.5%
O 359818
9.9%
E 336220
9.2%
I 319754
8.8%
R 274467
 
7.5%
S 261561
 
7.2%
D 233915
 
6.4%
G 177345
 
4.9%
B 149208
 
4.1%
Other values (16) 712507
19.6%
Lowercase Letter
ValueCountFrequency (%)
o 21253
16.0%
e 19795
14.9%
a 17649
13.3%
n 15491
11.7%
i 10033
7.6%
t 8882
6.7%
r 5967
 
4.5%
g 5756
 
4.3%
l 5167
 
3.9%
b 2952
 
2.2%
Other values (16) 19486
14.7%
Decimal Number
ValueCountFrequency (%)
1 87
35.2%
0 30
 
12.1%
2 24
 
9.7%
6 23
 
9.3%
7 23
 
9.3%
9 15
 
6.1%
4 15
 
6.1%
5 12
 
4.9%
8 9
 
3.6%
3 9
 
3.6%
Other Punctuation
ValueCountFrequency (%)
, 687
75.3%
. 149
 
16.3%
' 51
 
5.6%
/ 16
 
1.8%
? 4
 
0.4%
& 3
 
0.3%
@ 2
 
0.2%
Space Separator
ValueCountFrequency (%)
184862
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 642
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Math Symbol
ValueCountFrequency (%)
< 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3768063
95.3%
Common 186709
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 430200
11.4%
N 380637
10.1%
O 359818
9.5%
E 336220
 
8.9%
I 319754
 
8.5%
R 274467
 
7.3%
S 261561
 
6.9%
D 233915
 
6.2%
G 177345
 
4.7%
B 149208
 
4.0%
Other values (42) 844938
22.4%
Common
ValueCountFrequency (%)
184862
99.0%
, 687
 
0.4%
- 642
 
0.3%
. 149
 
0.1%
1 87
 
< 0.1%
' 51
 
< 0.1%
0 30
 
< 0.1%
( 26
 
< 0.1%
2 24
 
< 0.1%
6 23
 
< 0.1%
Other values (14) 128
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3954772
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 430200
10.9%
N 380637
 
9.6%
O 359818
 
9.1%
E 336220
 
8.5%
I 319754
 
8.1%
R 274467
 
6.9%
S 261561
 
6.6%
D 233915
 
5.9%
184862
 
4.7%
G 177345
 
4.5%
Other values (66) 995993
25.2%
Distinct99
Distinct (%)< 0.1%
Missing1714
Missing (%)0.3%
Memory size3.7 MiB
2024-02-16T00:24:31.636616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters978496
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 461576
94.3%
cz 7453
 
1.5%
be 2959
 
0.6%
de 2083
 
0.4%
gb 2078
 
0.4%
mx 1871
 
0.4%
au 1223
 
0.2%
ch 887
 
0.2%
ca 786
 
0.2%
sg 543
 
0.1%
Other values (89) 7789
 
1.6%
2024-02-16T00:24:32.039312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 462970
47.3%
S 462330
47.2%
C 9961
 
1.0%
Z 7721
 
0.8%
E 5840
 
0.6%
B 5603
 
0.6%
D 3320
 
0.3%
G 3218
 
0.3%
M 2846
 
0.3%
A 2782
 
0.3%
Other values (16) 11905
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 978496
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 462970
47.3%
S 462330
47.2%
C 9961
 
1.0%
Z 7721
 
0.8%
E 5840
 
0.6%
B 5603
 
0.6%
D 3320
 
0.3%
G 3218
 
0.3%
M 2846
 
0.3%
A 2782
 
0.3%
Other values (16) 11905
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 978496
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 462970
47.3%
S 462330
47.2%
C 9961
 
1.0%
Z 7721
 
0.8%
E 5840
 
0.6%
B 5603
 
0.6%
D 3320
 
0.3%
G 3218
 
0.3%
M 2846
 
0.3%
A 2782
 
0.3%
Other values (16) 11905
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 978496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 462970
47.3%
S 462330
47.2%
C 9961
 
1.0%
Z 7721
 
0.8%
E 5840
 
0.6%
B 5603
 
0.6%
D 3320
 
0.3%
G 3218
 
0.3%
M 2846
 
0.3%
A 2782
 
0.3%
Other values (16) 11905
 
1.2%
Distinct14797
Distinct (%)3.2%
Missing21624
Missing (%)4.4%
Memory size3.7 MiB
2024-02-16T00:24:32.368156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26
Mean length13.859879
Min length7

Characters and Unicode

Total characters6504968
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6427 ?
Unique (%)1.4%

Sample

1st rowChristopher Delgado
2nd rowChristopher Delgado
3rd rowChristine Chang
4th rowSarah Mason
5th rowSarah Mason
ValueCountFrequency (%)
james 50943
 
5.3%
franklin 47895
 
5.0%
christine 41662
 
4.3%
rice 41580
 
4.3%
johnson 41361
 
4.3%
jennifer 41256
 
4.3%
christopher 14641
 
1.5%
hernandez 13957
 
1.4%
brown 13840
 
1.4%
mrs 12879
 
1.3%
Other values (1577) 642942
66.8%
2024-02-16T00:24:32.949439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 664554
 
10.2%
n 646458
 
9.9%
493618
 
7.6%
a 473791
 
7.3%
r 470245
 
7.2%
i 467619
 
7.2%
s 349397
 
5.4%
o 335198
 
5.2%
l 268705
 
4.1%
h 224182
 
3.4%
Other values (44) 2111201
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5021860
77.2%
Uppercase Letter 973444
 
15.0%
Space Separator 493618
 
7.6%
Other Punctuation 16046
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 664554
13.2%
n 646458
12.9%
a 473791
9.4%
r 470245
9.4%
i 467619
9.3%
s 349397
 
7.0%
o 335198
 
6.7%
l 268705
 
5.4%
h 224182
 
4.5%
t 202492
 
4.0%
Other values (16) 919219
18.3%
Uppercase Letter
ValueCountFrequency (%)
J 209287
21.5%
C 92391
9.5%
R 89294
9.2%
M 76332
 
7.8%
S 59672
 
6.1%
F 57817
 
5.9%
H 53799
 
5.5%
A 46718
 
4.8%
B 43238
 
4.4%
D 37712
 
3.9%
Other values (16) 207184
21.3%
Space Separator
ValueCountFrequency (%)
493618
100.0%
Other Punctuation
ValueCountFrequency (%)
. 16046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5995304
92.2%
Common 509664
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 664554
 
11.1%
n 646458
 
10.8%
a 473791
 
7.9%
r 470245
 
7.8%
i 467619
 
7.8%
s 349397
 
5.8%
o 335198
 
5.6%
l 268705
 
4.5%
h 224182
 
3.7%
J 209287
 
3.5%
Other values (42) 1885868
31.5%
Common
ValueCountFrequency (%)
493618
96.9%
. 16046
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6504968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 664554
 
10.2%
n 646458
 
9.9%
493618
 
7.6%
a 473791
 
7.3%
r 470245
 
7.2%
i 467619
 
7.2%
s 349397
 
5.4%
o 335198
 
5.2%
l 268705
 
4.1%
h 224182
 
3.4%
Other values (44) 2111201
32.5%

DESTINATION_ZIP
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9393
Missing (%)1.9%
Memory size3.7 MiB

DUTIES_AND_TAXES_CHARGES
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct9957
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.585278
Minimum-19.71
Maximum50488.006
Zeros459405
Zeros (%)93.6%
Negative26
Negative (%)< 0.1%
Memory size3.7 MiB
2024-02-16T00:24:33.170571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-19.71
5-th percentile0
Q10
median0
Q30
95-th percentile31.982103
Maximum50488.006
Range50507.716
Interquartile range (IQR)0

Descriptive statistics

Standard deviation687.02468
Coefficient of variation (CV)11.339796
Kurtosis1639.7486
Mean60.585278
Median Absolute Deviation (MAD)0
Skewness33.583184
Sum29745069
Variance472002.91
MonotonicityNot monotonic
2024-02-16T00:24:33.496022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 459405
93.6%
19.71 4604
 
0.9%
38.97324 187
 
< 0.1%
35.78022 168
 
< 0.1%
45.99 158
 
< 0.1%
36.4635 142
 
< 0.1%
35.20206 137
 
< 0.1%
13.14 55
 
< 0.1%
25.59672 50
 
< 0.1%
41.61438 48
 
< 0.1%
Other values (9947) 26008
 
5.3%
ValueCountFrequency (%)
-19.71 12
 
< 0.1%
-17.35794 2
 
< 0.1%
-11.57634 2
 
< 0.1%
-9.198 2
 
< 0.1%
-8.69868 2
 
< 0.1%
-8.61984 2
 
< 0.1%
-7.21386 2
 
< 0.1%
-4.70412 2
 
< 0.1%
0 459405
93.6%
0.09198 2
 
< 0.1%
ValueCountFrequency (%)
50488.00596 4
< 0.1%
48382.09758 4
< 0.1%
46701.11052 4
< 0.1%
42525.19224 4
< 0.1%
41738.39532 4
< 0.1%
39861.51714 3
< 0.1%
39306.03678 4
< 0.1%
38352.92688 4
< 0.1%
37036.8639 2
< 0.1%
36297.5418 4
< 0.1%

HEIGHT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct139
Distinct (%)0.1%
Missing294158
Missing (%)59.9%
Infinite0
Infinite (%)0.0%
Mean10.831787
Minimum0
Maximum302
Zeros524
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2024-02-16T00:24:33.612894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q16
median8
Q312
95-th percentile25
Maximum302
Range302
Interquartile range (IQR)6

Descriptive statistics

Standard deviation10.915439
Coefficient of variation (CV)1.0077228
Kurtosis47.152331
Mean10.831787
Median Absolute Deviation (MAD)3
Skewness5.5305245
Sum2131739
Variance119.14681
MonotonicityNot monotonic
2024-02-16T00:24:33.729797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 22408
 
4.6%
6 20912
 
4.3%
8 19727
 
4.0%
12 18681
 
3.8%
7 17367
 
3.5%
10 14116
 
2.9%
4 12659
 
2.6%
9 10776
 
2.2%
13 8353
 
1.7%
11 6678
 
1.4%
Other values (129) 45127
 
9.2%
(Missing) 294158
59.9%
ValueCountFrequency (%)
0 524
 
0.1%
1 2870
 
0.6%
2 2356
 
0.5%
3 4301
 
0.9%
4 12659
2.6%
5 22408
4.6%
6 20912
4.3%
7 17367
3.5%
8 19727
4.0%
9 10776
2.2%
ValueCountFrequency (%)
302 1
 
< 0.1%
213 2
 
< 0.1%
177 1
 
< 0.1%
168 1
 
< 0.1%
165 1
 
< 0.1%
160 77
< 0.1%
156 2
 
< 0.1%
155 2
 
< 0.1%
153 6
 
< 0.1%
150 1
 
< 0.1%

LENGTH
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct152
Distinct (%)0.1%
Missing294158
Missing (%)59.9%
Infinite0
Infinite (%)0.0%
Mean19.500025
Minimum0
Maximum256
Zeros330
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2024-02-16T00:24:33.846273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q111
median14
Q324
95-th percentile45
Maximum256
Range256
Interquartile range (IQR)13

Descriptive statistics

Standard deviation15.397902
Coefficient of variation (CV)0.78963495
Kurtosis34.919621
Mean19.500025
Median Absolute Deviation (MAD)5
Skewness4.075631
Sum3837683
Variance237.09537
MonotonicityNot monotonic
2024-02-16T00:24:33.969399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 28718
 
5.8%
10 14558
 
3.0%
8 12911
 
2.6%
11 9627
 
2.0%
13 8956
 
1.8%
15 8168
 
1.7%
14 7934
 
1.6%
9 7589
 
1.5%
16 7460
 
1.5%
20 6770
 
1.4%
Other values (142) 84113
 
17.1%
(Missing) 294158
59.9%
ValueCountFrequency (%)
0 330
 
0.1%
1 707
 
0.1%
2 1102
 
0.2%
3 165
 
< 0.1%
4 559
 
0.1%
5 422
 
0.1%
6 3953
 
0.8%
7 3212
 
0.7%
8 12911
2.6%
9 7589
1.5%
ValueCountFrequency (%)
256 1
 
< 0.1%
250 83
< 0.1%
216 9
 
< 0.1%
215 1
 
< 0.1%
213 5
 
< 0.1%
209 19
 
< 0.1%
207 1
 
< 0.1%
200 2
 
< 0.1%
194 2
 
< 0.1%
193 2
 
< 0.1%

MODE
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
Ground
356295 
Air
134667 

Length

Max length6
Median length6
Mean length5.1771237
Min length3

Characters and Unicode

Total characters2541771
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAir
2nd rowAir
3rd rowGround
4th rowGround
5th rowGround

Common Values

ValueCountFrequency (%)
Ground 356295
72.6%
Air 134667
 
27.4%

Length

2024-02-16T00:24:34.092421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T00:24:34.187649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ground 356295
72.6%
air 134667
 
27.4%

Most occurring characters

ValueCountFrequency (%)
r 490962
19.3%
G 356295
14.0%
o 356295
14.0%
u 356295
14.0%
n 356295
14.0%
d 356295
14.0%
A 134667
 
5.3%
i 134667
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2050809
80.7%
Uppercase Letter 490962
 
19.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 490962
23.9%
o 356295
17.4%
u 356295
17.4%
n 356295
17.4%
d 356295
17.4%
i 134667
 
6.6%
Uppercase Letter
ValueCountFrequency (%)
G 356295
72.6%
A 134667
 
27.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 2541771
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 490962
19.3%
G 356295
14.0%
o 356295
14.0%
u 356295
14.0%
n 356295
14.0%
d 356295
14.0%
A 134667
 
5.3%
i 134667
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2541771
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 490962
19.3%
G 356295
14.0%
o 356295
14.0%
u 356295
14.0%
n 356295
14.0%
d 356295
14.0%
A 134667
 
5.3%
i 134667
 
5.3%
Distinct2622
Distinct (%)0.5%
Missing766
Missing (%)0.2%
Memory size3.7 MiB
2024-02-16T00:24:34.372707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length23
Mean length8.6240749
Min length3

Characters and Unicode

Total characters4227487
Distinct characters63
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique693 ?
Unique (%)0.1%

Sample

1st rowMABANK
2nd rowMABANK
3rd rowMABANK
4th rowMCKINNEY
5th rowMCKINNEY
ValueCountFrequency (%)
san 103959
 
15.1%
diego 99930
 
14.5%
fairburn 62507
 
9.1%
ontario 19088
 
2.8%
lake 14144
 
2.1%
city 11283
 
1.6%
mabank 9939
 
1.4%
forest 9667
 
1.4%
santa 9498
 
1.4%
houston 9330
 
1.4%
Other values (2244) 339031
49.3%
2024-02-16T00:24:34.741603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 428484
 
10.1%
N 394288
 
9.3%
E 319496
 
7.6%
I 317653
 
7.5%
O 312786
 
7.4%
R 295974
 
7.0%
S 253723
 
6.0%
198180
 
4.7%
D 177578
 
4.2%
L 170336
 
4.0%
Other values (53) 1358989
32.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3723582
88.1%
Lowercase Letter 305252
 
7.2%
Space Separator 198180
 
4.7%
Dash Punctuation 285
 
< 0.1%
Other Punctuation 166
 
< 0.1%
Decimal Number 22
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 428484
11.5%
N 394288
10.6%
E 319496
 
8.6%
I 317653
 
8.5%
O 312786
 
8.4%
R 295974
 
7.9%
S 253723
 
6.8%
D 177578
 
4.8%
L 170336
 
4.6%
T 164207
 
4.4%
Other values (16) 889057
23.9%
Lowercase Letter
ValueCountFrequency (%)
o 37809
12.4%
n 37554
12.3%
a 35134
11.5%
e 32228
10.6%
i 32029
10.5%
g 24269
8.0%
t 17144
 
5.6%
r 16212
 
5.3%
s 12167
 
4.0%
l 11356
 
3.7%
Other values (15) 49350
16.2%
Decimal Number
ValueCountFrequency (%)
2 10
45.5%
1 5
22.7%
3 2
 
9.1%
0 2
 
9.1%
8 1
 
4.5%
9 1
 
4.5%
4 1
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 157
94.6%
, 7
 
4.2%
/ 2
 
1.2%
Space Separator
ValueCountFrequency (%)
198180
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 285
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4028834
95.3%
Common 198653
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 428484
 
10.6%
N 394288
 
9.8%
E 319496
 
7.9%
I 317653
 
7.9%
O 312786
 
7.8%
R 295974
 
7.3%
S 253723
 
6.3%
D 177578
 
4.4%
L 170336
 
4.2%
T 164207
 
4.1%
Other values (41) 1194309
29.6%
Common
ValueCountFrequency (%)
198180
99.8%
- 285
 
0.1%
. 157
 
0.1%
2 10
 
< 0.1%
, 7
 
< 0.1%
1 5
 
< 0.1%
/ 2
 
< 0.1%
3 2
 
< 0.1%
0 2
 
< 0.1%
8 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4227487
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 428484
 
10.1%
N 394288
 
9.3%
E 319496
 
7.6%
I 317653
 
7.5%
O 312786
 
7.4%
R 295974
 
7.0%
S 253723
 
6.0%
198180
 
4.7%
D 177578
 
4.2%
L 170336
 
4.0%
Other values (53) 1358989
32.1%
Distinct55
Distinct (%)< 0.1%
Missing124
Missing (%)< 0.1%
Memory size3.7 MiB
2024-02-16T00:24:34.862093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters981676
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 471318
96.0%
gb 9659
 
2.0%
de 1769
 
0.4%
be 1691
 
0.3%
it 1461
 
0.3%
ca 1168
 
0.2%
nl 1126
 
0.2%
ch 948
 
0.2%
cz 758
 
0.2%
mx 478
 
0.1%
Other values (45) 462
 
0.1%
2024-02-16T00:24:35.083327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 471479
48.0%
U 471338
48.0%
B 11372
 
1.2%
G 9685
 
1.0%
E 3610
 
0.4%
C 2913
 
0.3%
D 1784
 
0.2%
T 1531
 
0.2%
I 1493
 
0.2%
A 1251
 
0.1%
Other values (16) 5220
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 981676
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 471479
48.0%
U 471338
48.0%
B 11372
 
1.2%
G 9685
 
1.0%
E 3610
 
0.4%
C 2913
 
0.3%
D 1784
 
0.2%
T 1531
 
0.2%
I 1493
 
0.2%
A 1251
 
0.1%
Other values (16) 5220
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 981676
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 471479
48.0%
U 471338
48.0%
B 11372
 
1.2%
G 9685
 
1.0%
E 3610
 
0.4%
C 2913
 
0.3%
D 1784
 
0.2%
T 1531
 
0.2%
I 1493
 
0.2%
A 1251
 
0.1%
Other values (16) 5220
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 981676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 471479
48.0%
U 471338
48.0%
B 11372
 
1.2%
G 9685
 
1.0%
E 3610
 
0.4%
C 2913
 
0.3%
D 1784
 
0.2%
T 1531
 
0.2%
I 1493
 
0.2%
A 1251
 
0.1%
Other values (16) 5220
 
0.5%

ORIGIN_SENDER_NAME2
Real number (ℝ)

Distinct5742
Distinct (%)1.2%
Missing1504
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean4.8186745 × 1012
Minimum4.068914 × 108
Maximum9.9994377 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2024-02-16T00:24:35.236263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.068914 × 108
5-th percentile5.2253942 × 1011
Q12.6058437 × 1012
median4.2706907 × 1012
Q37.2160595 × 1012
95-th percentile9.7302183 × 1012
Maximum9.9994377 × 1012
Range9.9990308 × 1012
Interquartile range (IQR)4.6102158 × 1012

Descriptive statistics

Standard deviation2.7934191 × 1012
Coefficient of variation (CV)0.57970696
Kurtosis-1.003026
Mean4.8186745 × 1012
Median Absolute Deviation (MAD)2.1953369 × 1012
Skewness0.19587517
Sum2.3585388 × 1018
Variance7.8031905 × 1024
MonotonicityNot monotonic
2024-02-16T00:24:35.398274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.712043637 × 101247771
 
9.7%
9.730218283 × 101216510
 
3.4%
5.776435396 × 101214482
 
2.9%
7.253516149 × 101214029
 
2.9%
9.415680895 × 10129555
 
1.9%
5.558939569 × 10129271
 
1.9%
5.699567853 × 10118876
 
1.8%
5.313890467 × 10128854
 
1.8%
3.4987932 × 10127429
 
1.5%
7.564786118 × 10127267
 
1.5%
Other values (5732) 345414
70.4%
ValueCountFrequency (%)
406891400 4
 
< 0.1%
1259439177 1
 
< 0.1%
4146075779 1
 
< 0.1%
4413164861 1
 
< 0.1%
6915000203 2
 
< 0.1%
8207031698 1
 
< 0.1%
8753216181 1
 
< 0.1%
9305079568 1
 
< 0.1%
1.3367867 × 10103
 
< 0.1%
1.353208903 × 1010210
< 0.1%
ValueCountFrequency (%)
9.999437707 × 10129
 
< 0.1%
9.994852334 × 10121
 
< 0.1%
9.994687059 × 10121
 
< 0.1%
9.992078515 × 10122
 
< 0.1%
9.990621479 × 10121
 
< 0.1%
9.9848107 × 1012429
0.1%
9.983650091 × 10125
 
< 0.1%
9.981423103 × 10121
 
< 0.1%
9.979948911 × 101248
 
< 0.1%
9.976591847 × 10124
 
< 0.1%

ORIGIN_ZIP
Unsupported

REJECTED  UNSUPPORTED 

Missing131
Missing (%)< 0.1%
Memory size3.7 MiB

SHIP_DATE
Date

MISSING 

Distinct221129
Distinct (%)46.4%
Missing14209
Missing (%)2.9%
Memory size3.7 MiB
Minimum2018-09-20 00:00:00
Maximum2024-01-19 21:39:00
2024-02-16T00:24:35.555563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:35.705400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SHIP_DATE_DAY_YEAR
Date

MISSING 

Distinct1504
Distinct (%)0.3%
Missing14209
Missing (%)2.9%
Memory size3.7 MiB
Minimum2018-09-20 00:00:00
Maximum2024-01-19 00:00:00
2024-02-16T00:24:35.850744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:36.038420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SHIP_WEIGHT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1111
Distinct (%)0.2%
Missing852
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean24.928861
Minimum0
Maximum5662
Zeros13078
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2024-02-16T00:24:36.236336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median10
Q327
95-th percentile75
Maximum5662
Range5662
Interquartile range (IQR)24

Descriptive statistics

Standard deviation72.806616
Coefficient of variation (CV)2.9205753
Kurtosis478.59252
Mean24.928861
Median Absolute Deviation (MAD)8
Skewness16.767302
Sum12217884
Variance5300.8033
MonotonicityNot monotonic
2024-02-16T00:24:36.465304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 49650
 
10.1%
1 39926
 
8.1%
3 32429
 
6.6%
4 22682
 
4.6%
5 21330
 
4.3%
6 19822
 
4.0%
7 17014
 
3.5%
0 13078
 
2.7%
10 13001
 
2.6%
8 12823
 
2.6%
Other values (1101) 248355
50.6%
ValueCountFrequency (%)
0 13078
 
2.7%
1 39926
8.1%
2 49650
10.1%
3 32429
6.6%
4 22682
4.6%
5 21330
4.3%
6 19822
 
4.0%
7 17014
 
3.5%
8 12823
 
2.6%
9 11471
 
2.3%
ValueCountFrequency (%)
5662 1
< 0.1%
5102 1
< 0.1%
3835 1
< 0.1%
3776 1
< 0.1%
3707 1
< 0.1%
3360 1
< 0.1%
3200 1
< 0.1%
3144 1
< 0.1%
3123 1
< 0.1%
2966 2
< 0.1%

SHIP_WEIGHT_UNIT_OF_MEASURE
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
POUNDS
449874 
KILOGRAMS
 
21768
OUNCES
 
19320

Length

Max length9
Median length6
Mean length6.1330123
Min length6

Characters and Unicode

Total characters3011076
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOUNDS
2nd rowPOUNDS
3rd rowPOUNDS
4th rowPOUNDS
5th rowPOUNDS

Common Values

ValueCountFrequency (%)
POUNDS 449874
91.6%
KILOGRAMS 21768
 
4.4%
OUNCES 19320
 
3.9%

Length

2024-02-16T00:24:36.699398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-16T00:24:36.899168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pounds 449874
91.6%
kilograms 21768
 
4.4%
ounces 19320
 
3.9%

Most occurring characters

ValueCountFrequency (%)
O 490962
16.3%
S 490962
16.3%
U 469194
15.6%
N 469194
15.6%
P 449874
14.9%
D 449874
14.9%
K 21768
 
0.7%
I 21768
 
0.7%
L 21768
 
0.7%
G 21768
 
0.7%
Other values (5) 103944
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3011076
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 490962
16.3%
S 490962
16.3%
U 469194
15.6%
N 469194
15.6%
P 449874
14.9%
D 449874
14.9%
K 21768
 
0.7%
I 21768
 
0.7%
L 21768
 
0.7%
G 21768
 
0.7%
Other values (5) 103944
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 3011076
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 490962
16.3%
S 490962
16.3%
U 469194
15.6%
N 469194
15.6%
P 449874
14.9%
D 449874
14.9%
K 21768
 
0.7%
I 21768
 
0.7%
L 21768
 
0.7%
G 21768
 
0.7%
Other values (5) 103944
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3011076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 490962
16.3%
S 490962
16.3%
U 469194
15.6%
N 469194
15.6%
P 449874
14.9%
D 449874
14.9%
K 21768
 
0.7%
I 21768
 
0.7%
L 21768
 
0.7%
G 21768
 
0.7%
Other values (5) 103944
 
3.5%

TOTAL_PAID
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct31227
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.04429
Minimum-13210.207
Maximum75973.181
Zeros37073
Zeros (%)7.6%
Negative1308
Negative (%)0.3%
Memory size3.7 MiB
2024-02-16T00:24:37.180385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-13210.207
5-th percentile0
Q16.93792
median10.1835
Q325.41276
95-th percentile343.4001
Maximum75973.181
Range89183.388
Interquartile range (IQR)18.47484

Descriptive statistics

Standard deviation879.86033
Coefficient of variation (CV)7.4536459
Kurtosis1321.9818
Mean118.04429
Median Absolute Deviation (MAD)3.85002
Skewness28.923489
Sum57955260
Variance774154.2
MonotonicityNot monotonic
2024-02-16T00:24:37.424468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37073
 
7.6%
6.80652 12687
 
2.6%
6.68826 4866
 
1.0%
6.84594 3506
 
0.7%
6.93792 3356
 
0.7%
6.66198 3164
 
0.6%
6.7671 3024
 
0.6%
6.92478 3001
 
0.6%
6.88536 2842
 
0.6%
6.8985 2706
 
0.6%
Other values (31217) 414737
84.5%
ValueCountFrequency (%)
-13210.20702 1
< 0.1%
-750.05748 1
< 0.1%
-526.02048 1
< 0.1%
-413.02962 1
< 0.1%
-325.50408 1
< 0.1%
-324.17694 1
< 0.1%
-253.57572 1
< 0.1%
-237.7026 1
< 0.1%
-235.25856 1
< 0.1%
-222.63102 1
< 0.1%
ValueCountFrequency (%)
75973.1805 1
 
< 0.1%
68001.10308 2
< 0.1%
56563.98138 4
< 0.1%
54609.36696 4
< 0.1%
53059.16232 4
< 0.1%
50859.10584 4
< 0.1%
48814.14078 4
< 0.1%
44205.43032 4
< 0.1%
43615.56258 3
< 0.1%
42210.1476 2
< 0.1%
Distinct209873
Distinct (%)42.7%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
2024-02-16T00:24:37.728801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length13.28049
Min length6

Characters and Unicode

Total characters6520216
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122753 ?
Unique (%)25.0%

Sample

1st rowPaul Hayes
2nd rowMadison Anderson
3rd rowJoseph Larson
4th rowBryan Farley
5th rowBryan Farley
ValueCountFrequency (%)
michael 11275
 
1.1%
smith 10820
 
1.1%
james 8357
 
0.8%
johnson 8311
 
0.8%
david 7741
 
0.8%
john 7095
 
0.7%
jennifer 7085
 
0.7%
williams 6931
 
0.7%
christopher 6725
 
0.7%
thomas 6707
 
0.7%
Other values (1588) 922889
91.9%
2024-02-16T00:24:38.244558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 605533
 
9.3%
a 601540
 
9.2%
512974
 
7.9%
n 489291
 
7.5%
r 468816
 
7.2%
i 394719
 
6.1%
o 351889
 
5.4%
l 332090
 
5.1%
s 294074
 
4.5%
t 226906
 
3.5%
Other values (44) 2242384
34.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4976853
76.3%
Uppercase Letter 1020050
 
15.6%
Space Separator 512974
 
7.9%
Other Punctuation 10339
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 605533
12.2%
a 601540
12.1%
n 489291
9.8%
r 468816
9.4%
i 394719
 
7.9%
o 351889
 
7.1%
l 332090
 
6.7%
s 294074
 
5.9%
t 226906
 
4.6%
h 219734
 
4.4%
Other values (16) 992261
19.9%
Uppercase Letter
ValueCountFrequency (%)
M 113634
 
11.1%
J 101087
 
9.9%
S 83639
 
8.2%
C 76140
 
7.5%
D 68300
 
6.7%
B 63805
 
6.3%
R 63043
 
6.2%
A 62922
 
6.2%
W 47979
 
4.7%
H 46658
 
4.6%
Other values (16) 292843
28.7%
Space Separator
ValueCountFrequency (%)
512974
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10339
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5996903
92.0%
Common 523313
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 605533
 
10.1%
a 601540
 
10.0%
n 489291
 
8.2%
r 468816
 
7.8%
i 394719
 
6.6%
o 351889
 
5.9%
l 332090
 
5.5%
s 294074
 
4.9%
t 226906
 
3.8%
h 219734
 
3.7%
Other values (42) 2012311
33.6%
Common
ValueCountFrequency (%)
512974
98.0%
. 10339
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6520216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 605533
 
9.3%
a 601540
 
9.2%
512974
 
7.9%
n 489291
 
7.5%
r 468816
 
7.2%
i 394719
 
6.1%
o 351889
 
5.4%
l 332090
 
5.1%
s 294074
 
4.5%
t 226906
 
3.5%
Other values (44) 2242384
34.4%

WIDTH
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct119
Distinct (%)0.1%
Missing294158
Missing (%)59.9%
Infinite0
Infinite (%)0.0%
Mean15.374423
Minimum1
Maximum244
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 MiB
2024-02-16T00:24:38.511668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median12
Q317
95-th percentile35
Maximum244
Range243
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.306076
Coefficient of variation (CV)0.7353821
Kurtosis25.635513
Mean15.374423
Median Absolute Deviation (MAD)4
Skewness3.8070752
Sum3025748
Variance127.82735
MonotonicityNot monotonic
2024-02-16T00:24:38.804143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 31623
 
6.4%
10 21138
 
4.3%
8 18052
 
3.7%
13 11845
 
2.4%
9 10829
 
2.2%
6 10043
 
2.0%
16 8878
 
1.8%
14 8177
 
1.7%
11 7470
 
1.5%
7 7401
 
1.5%
Other values (109) 61348
 
12.5%
(Missing) 294158
59.9%
ValueCountFrequency (%)
1 454
 
0.1%
2 22
 
< 0.1%
3 555
 
0.1%
4 1561
 
0.3%
5 2201
 
0.4%
6 10043
2.0%
7 7401
 
1.5%
8 18052
3.7%
9 10829
2.2%
10 21138
4.3%
ValueCountFrequency (%)
244 1
 
< 0.1%
155 1
 
< 0.1%
153 1
 
< 0.1%
150 7
 
< 0.1%
145 86
< 0.1%
143 27
 
< 0.1%
137 12
 
< 0.1%
127 4
 
< 0.1%
126 4
 
< 0.1%
125 38
< 0.1%

Interactions

2024-02-16T00:24:21.284319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:01.372259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:03.104815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:05.226294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:06.912972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:08.761564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:12.488159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:16.245130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:19.049445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:21.503224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:01.656298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:03.357022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:05.366138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:07.123728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:09.107427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:12.868375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:16.568210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:19.336390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:21.749629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:01.977167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:03.630583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:05.517989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:07.342626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:09.556743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:13.309644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:16.880289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:19.620374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:21.944878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:02.105760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:03.828539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:05.650222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:07.544587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:09.871101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:13.737737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:17.112655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:19.810399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:22.142658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:02.221618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:04.056706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:05.798678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:07.734608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:10.170512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:14.113790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:17.342303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:20.053175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:22.376888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:02.373655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:04.311393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:05.984652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:07.924239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:10.612011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:14.487214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:17.892856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:20.343568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:22.606849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:02.571796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:04.561477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:06.249522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:08.109065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:10.985651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:14.897879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:18.197945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:20.641418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:22.822778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:02.762118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:04.816826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:06.514445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:08.265861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:11.379718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:15.438644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:18.523790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:20.868428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:23.047766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:02.896356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:05.009033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:06.716685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:08.497025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:11.849565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:15.793764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:18.766440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-16T00:24:21.076887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-02-16T00:24:39.000085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACCESSORIAL_CHARGECARRIER_NAME2DUTIES_AND_TAXES_CHARGESHEIGHTLENGTHMODEORIGIN_SENDER_NAME2SHIP_WEIGHTSHIP_WEIGHT_UNIT_OF_MEASURETOTAL_PAIDUnnamed: 0WIDTH
ACCESSORIAL_CHARGE1.0000.0080.3810.2580.3190.040-0.0480.1610.0110.664-0.2330.352
CARRIER_NAME20.0081.0000.7420.2540.0540.530-0.1050.0490.4680.470-0.5240.213
DUTIES_AND_TAXES_CHARGES0.3810.7421.0000.3040.1710.073-0.1250.1360.0540.412-0.3820.295
HEIGHT0.2580.2540.3041.0000.3930.306-0.0500.5360.4420.408-0.1400.525
LENGTH0.3190.0540.1710.3931.0000.145-0.0950.6280.2950.376-0.0310.723
MODE0.0400.5300.0730.3060.1451.0000.0830.0200.401-0.5160.256-0.149
ORIGIN_SENDER_NAME2-0.048-0.105-0.125-0.050-0.0950.0831.000-0.0890.204-0.0740.013-0.109
SHIP_WEIGHT0.1610.0490.1360.5360.6280.020-0.0891.0000.0330.295-0.0120.576
SHIP_WEIGHT_UNIT_OF_MEASURE0.0110.4680.0540.4420.2950.4010.2040.0331.000-0.2590.237-0.222
TOTAL_PAID0.6640.4700.4120.4080.376-0.516-0.0740.295-0.2591.000-0.2510.456
Unnamed: 0-0.233-0.524-0.382-0.140-0.0310.2560.013-0.0120.237-0.2511.000-0.121
WIDTH0.3520.2130.2950.5250.723-0.149-0.1090.576-0.2220.456-0.1211.000

Missing values

2024-02-16T00:24:23.531119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-16T00:24:24.947485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-16T00:24:27.349702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0ACCESSORIAL_CHARGECARRIER_NAME2DELIVERY_DATEDESTINATION_CITYDESTINATION_COUNTRYDESTINATION_RECEIVER_NAME2DESTINATION_ZIPDUTIES_AND_TAXES_CHARGESHEIGHTLENGTHMODEORIGIN_CITYORIGIN_COUNTRYORIGIN_SENDER_NAME2ORIGIN_ZIPSHIP_DATESHIP_DATE_DAY_YEARSHIP_WEIGHTSHIP_WEIGHT_UNIT_OF_MEASURETOTAL_PAIDTRACKING_NUMBER2WIDTH
000.0000Randall Hall2020-11-04DESOTOUSChristopher Delgado751150.0NaNNaNAirMABANKUS3.712044e+12751472020-11-03 15:52:002020-11-031.0POUNDS13.82328Paul HayesNaN
110.0000Randall Hall2020-11-02DESOTOUSChristopher Delgado751150.0NaNNaNAirMABANKUS3.712044e+12751472020-10-30 14:53:002020-10-301.0POUNDS13.82328Madison AndersonNaN
223.9420Randall Hall2020-10-26SAINT ALBANSUSChristine Chang251770.0NaNNaNGroundMABANKUS3.712044e+12751472020-10-22 14:15:002020-10-22NaNPOUNDS4.16538Joseph LarsonNaN
3341.4567Randall Hall2020-11-05BROKEN ARROWUSSarah Mason740120.06.010.0GroundMCKINNEYUS2.213039e+11750702020-11-03 15:44:002020-11-0335.0POUNDS65.66058Bryan Farley9.0
4417.9361Randall Hall2020-11-05BROKEN ARROWUSSarah Mason740120.06.010.0GroundMCKINNEYUS2.213039e+11750702020-11-03 15:44:002020-11-0335.0POUNDS40.78656Bryan Farley10.0
55137.9700Randall HallNaTLAREDOUSScott Washington780450.012.016.0AirFAIRBURNUS5.558940e+12302132020-11-03 16:25:002020-11-034.0POUNDS236.69082Aaron Smith12.0
660.0000Randall Hall2020-11-04FAIRBURNUSJennifer Johnson302130.0NaNNaNAirATLANTAUS7.079584e+12303542020-11-03 22:30:002020-11-034.0POUNDS20.20932Holly ConradNaN
770.0000Randall Hall2020-10-30SAN DIEGOUSJames Franklin921230.08.010.0GroundMIDLANDUS3.712044e+12797062020-10-27 11:45:002020-10-273.0POUNDS15.92568Danielle Stevens8.0
8811.8917Randall Hall2020-11-03AZTECUSJohn Green874100.0NaNNaNAirMIDLANDUS6.978112e+12797062020-11-02 13:23:002020-11-021.0POUNDS33.54642Peter RodriguezNaN
9928.2510Randall Hall2020-11-04CARRIZO SPRINGSUSAdam Harris788340.09.024.0AirMIDLANDUS5.963410e+12797062020-11-03 14:57:002020-11-0328.0POUNDS92.89980Aaron Smith20.0
Unnamed: 0ACCESSORIAL_CHARGECARRIER_NAME2DELIVERY_DATEDESTINATION_CITYDESTINATION_COUNTRYDESTINATION_RECEIVER_NAME2DESTINATION_ZIPDUTIES_AND_TAXES_CHARGESHEIGHTLENGTHMODEORIGIN_CITYORIGIN_COUNTRYORIGIN_SENDER_NAME2ORIGIN_ZIPSHIP_DATESHIP_DATE_DAY_YEARSHIP_WEIGHTSHIP_WEIGHT_UNIT_OF_MEASURETOTAL_PAIDTRACKING_NUMBER2WIDTH
4909521412728.22472Nicole BrooksNaTSLATONUSJenna Evans793640.08.08.0AirSAN DIEGOUS2.168663e+1292123NaTNaT0.0KILOGRAMS232.31520Katherine Reid8.0
490953141281.18260Nicole Brooks2024-01-17MABANKUSAndrew Gonzalez751470.04.08.0GroundSan DiegoUS5.057829e+12921232024-01-10 19:56:002024-01-102.0POUNDS8.21250Benjamin May8.0
490954141290.00000Nicole Brooks2024-01-17OCEANSIDEUSDaniel Stewart920560.09.09.0GroundPOMONAUS3.616013e+12917682024-01-16 21:37:002024-01-162.0POUNDS6.92478Courtney Strong9.0
490955141301.18260Nicole Brooks2024-01-16KALKASKAUSGail Cook496460.01.011.0GroundSAN DIEGOUS1.828520e+12921232024-01-08 20:47:002024-01-082.0POUNDS8.51472Patricia Howard9.0
490956141311.27458Nicole Brooks2024-01-18MABANKUSAndrew Gonzalez751470.04.012.0AirSAN DIEGOUS1.828520e+12921232024-01-17 16:51:002024-01-177.0POUNDS26.81874Bonnie Douglas12.0
490957141320.00000Nicole Brooks2024-01-18DESOTOUSEdwin Bowers751150.06.012.0AirSan DiegoUS5.057829e+12921232024-01-17 16:51:002024-01-174.0POUNDS20.51154Leslie Wilkinson12.0
490958141331.18260Nicole Brooks2024-01-16BALL GROUNDUSCarla Davis301070.01.011.0GroundSAN DIEGOUS1.828520e+12921232024-01-08 21:34:002024-01-082.0POUNDS8.51472Alvin Cowan9.0
490959141340.00000Nicole Brooks2024-01-16SEWICKLEYUSDaniel Graham151430.01.011.0GroundSAN DIEGOUS1.828520e+12921232024-01-08 21:39:002024-01-082.0POUNDS7.24014Ruth Dillon9.0
490960141350.00000Nicole Brooks2024-01-19MIAMIUSLauren Taylor331220.08.027.0AirSAN DIEGOUS1.828520e+12921232024-01-18 16:44:002024-01-1819.0POUNDS46.10826Juan Munoz17.0
490961141361.18260Nicole Brooks2024-01-16TOPEKAUSConnie Smith666180.04.036.0GroundSan DiegoUS5.057829e+12921232024-01-09 21:32:002024-01-0927.0POUNDS15.41322Stephanie Gonzalez4.0